Algorithmic Trading & Signal System
A Python trading and signal research system with MetaTrader 5 integration, PostgreSQL storage, Dockerized services, market-structure analysis, and robust error handling.
Problem
Retail trading experiments are often difficult to debug because signal logic, execution behavior, risk controls, and logs are mixed together or hidden inside opaque tools.
Solution
Built a modular Python system that separates market-data ingestion, signal evaluation, risk management, execution coordination, and structured logging so strategy behavior can be inspected and improved systematically.
What this proves
The most recruiter-relevant signals from this build, pulled from the project feature set.
Modular signal and market-structure analysis pipeline
Liquidity and fair-value-gap detection
Confluence scoring and session/news filtering
Volatility-adaptive stop-loss and partial-close logic
Overview
A Python-based algorithmic trading and signal research system built around the MetaTrader 5 platform. The project is structured as a modular pipeline for market-data processing, trade signal generation, risk management, and structured logging. It was developed as a B.Tech capstone project to investigate market-structure concepts such as fair-value gaps, liquidity analysis, and confluence scoring, with an emphasis on engineering rigor, diagnostics, and hypothesis-driven experimentation rather than live profitability.
Architecture
Python application organized around a central orchestrator with separate modules for market-data processing, market-structure analysis, risk management, and structured logging. MetaTrader 5 serves as the execution and data-feed environment, while PostgreSQL persists trade records and diagnostic outputs for offline analysis.
Screenshots
Key features
- Modular signal and market-structure analysis pipeline
- Liquidity and fair-value-gap detection
- Confluence scoring and session/news filtering
- Volatility-adaptive stop-loss and partial-close logic
- Structured trade logging and diagnostic tooling
- PostgreSQL persistence for trade history and analytics
Challenges
- Translating discretionary trading concepts into deterministic, testable modules
- Managing state and position tracking across MetaTrader 5 bar events
- Designing logging and diagnostics that reveal discrepancies between expected and observed behavior
Lessons learned
- Modular architecture makes it possible to isolate and test individual strategy components
- Structured logging is as important as the trading logic for understanding system behavior
- Risk management and debugging infrastructure deserve as much design attention as signal generation
Future improvements
- Expanded instrument coverage and multi-timeframe analysis
- Automated performance reporting and hypothesis tracking
- Visualization dashboard for strategy diagnostics